Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.

Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials and Methods A deep learning AI algorithm was developed and validated for DBT with retrospectively collected examinations (January 2010 to December 2021) from 14 institutions in the United States and South Korea. A multicenter reader study was performed to compare the performance of 15 radiologists (seven breast specialists, eight general radiologists) in interpreting DBT examinations in 258 women (mean age, 56 years ± 13.41 [SD]), including 65 cancer cases, with and without the use of AI. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were evaluated. Results The AUC for stand-alone AI performance was 0.93 (95% CI: 0.92, 0.94). With AI, radiologists' AUC improved from 0.90 (95% CI: 0.86, 0.93) to 0.92 (95% CI: 0.88, 0.96) (P = .003) in the reader study. AI showed higher specificity (89.64% [95% CI: 85.34%, 93.94%]) than radiologists (77.34% [95% CI: 75.82%, 78.87%]) (P < .001). When reading with AI, radiologists' sensitivity increased from 85.44% (95% CI: 83.22%, 87.65%) to 87.69% (95% CI: 85.63%, 89.75%) (P = .04), with no evidence of a difference in specificity. Reading time decreased from 54.41 seconds (95% CI: 52.56, 56.27) without AI to 48.52 seconds (95% CI: 46.79, 50.25) with AI (P < .001). Interreader agreement measured by Fleiss κ increased from 0.59 to 0.62. Conclusion The AI model showed better diagnostic accuracy than radiologists in breast cancer detection, as well as reduced reading times. The concurrent use of AI in DBT interpretation could improve both accuracy and efficiency. Keywords: Breast, Computer-Aided Diagnosis (CAD), Tomosynthesis, Artificial Intelligence, Digital Breast Tomosynthesis, Breast Cancer, Computer-Aided Detection, Screening Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Bae in this issue.

Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography.

OBJECTIVE: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. MATERIALS AND METHODS: From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10-49), 2 (50-69), 3 (70-89), and 4 (90-100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. RESULTS: Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). CONCLUSION: PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.

Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography.

Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results: Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion: Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.

Clinical Impact of Genomic and Pathway Alterations in Stage I EGFR-Mutant Lung Adenocarcinoma.

PURPOSE: We investigated the clinical impact of genomic and pathway alterations in stage I epidermal growth factor receptor (EGFR)-mutant lung adenocarcinomas, which have a high recurrence rate despite complete surgical resection. MATERIALS AND METHODS: Out of the initial cohort of 257 patients with completely resected stage I EGFR-mutant lung adenocarcinoma, tumor samples from 105 patients were subjected to analysis using large-panel next-generation sequencing. We analyzed 11 canonical oncogenic pathways and determined the number of pathway alterations (NPA). Survival analyses were performed based on co-occurring alterations and NPA in three patient groups: all patients, patients with International Association for the Study of Lung Cancer (IASLC) pathology grade 2, and patients with recurrent tumors treated with EGFR-tyrosine kinase inhibitor (TKI). RESULTS: In the univariate analysis, pathological stage, IASLC grade, TP53 mutation, NPA, phosphoinositide 3-kinase pathway, p53 pathway, and cell cycle pathway exhibited significant associations with worse recurrence-free survival (RFS). Moreover, RPS6KB1 or EGFR amplifications were linked to a poorer RFS. Multivariate analysis revealed that pathologic stage, IASLC grade, and cell cycle pathway alteration were independent poor prognostic factors for RFS (p=0.002, p < 0.001, and p=0.006, respectively). In the grade 2 subgroup, higher NPA was independently associated with worse RFS (p=0.003). Additionally, in patients with recurrence treated with EGFR-TKIs, co-occurring TP53 mutations were linked to shorter progression-free survival (p=0.025). CONCLUSION: Genomic and pathway alterations, particularly cell cycle alterations, high NPA, and TP53 mutations, were associated with worse clinical outcomes in stage I EGFR-mutant lung adenocarcinoma. These findings may have implications for risk stratification and the development of new therapeutic strategies in early-stage EGFR-mutant lung cancer patients.

Trends and Clinical Characteristics of Next-Generation Sequencing-Based Genetic Panel Tests: An Analysis of Korean Nationwide Claims Data.

PURPOSE: In the modern era of precision medicine, next-generation sequencing (NGS) is employed for a variety of clinical purposes. The aim of this study was to investigate the trends and clinical characteristics of NGS testing in South Korea. MATERIALS AND METHODS: This nationwide, population-based, retrospective cohort study examined National Health Insurance Service claims data from 2017 to 2021 for NGS and from 2008 to 2021 for gene-targeted anticancer drugs. RESULTS: Among the total 98,748 claims, there were 51,407 (52.1%) solid cancer panels, 30,173 (30.5%) hereditary disease panels, and 17,168 (17.4%) hematolymphoid cancer panels. The number of annual claims showed a persistent upward trend, exhibiting a 5.4-fold increase, from 5,436 in 2017 to 29,557 in 2021. In the solid cancer panel, colorectal cancer was the most common (19.2%), followed by lung cancer (18.8%). The annual claims for targeted cancer drugs have increased 25.7-fold, from 3,932 in 2008 to 101,211 in 2020. Drugs for the treatment of lung cancer accounted for 488,819 (71.9%) claims. The number of patients who received non-hereditary NGS testing has substantially increased, and among them, the count of patients prescribed targeted anticancer drugs consistently rose from 508 (13.9%) in 2017 to 2,245 (12.3%) in 2020. CONCLUSION: This study highlights the rising nationwide demand for comprehensive genetic testing for disease diagnosis and treatment following NGS reimbursement by the National Health Insurance in South Korea, in addition to the need for greater utilization of targeted anticancer drugs.

3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net.

Mammography is currently the most commonly used modality for breast cancer screening. However, its sensitivity is relatively low in women with dense breasts. Dense breast tissues show a relatively high rate of interval cancers and are at high risk for developing breast cancer. As a supplemental screening tool, ultrasonography is a widely adopted imaging modality to standard mammography, especially for dense breasts. Lately, automated breast ultrasound imaging has gained attention due to its advantages over hand-held ultrasound imaging. However, automated breast ultrasound imaging requires considerable time and effort for reading because of the lengthy data. Hence, developing a computer-aided nodule detection system for automated breast ultrasound is invaluable and impactful practically. This study proposes a three-dimensional breast nodule detection system based on a simple two-dimensional deep-learning model exploiting automated breast ultrasound. Additionally, we provide several postprocessing steps to reduce false positives. In our experiments using the in-house automated breast ultrasound datasets, a sensitivity of [Formula: see text] with 8.6 false positives is achieved on unseen test data at best.

Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence.

This study evaluated how often clinically significant lung nodules were detected unexpectedly on chest radiographs (CXR) by artificial intelligence (AI)-based detection software, and whether co-existing findings can aid in differential diagnosis of lung nodules. Patients (> 18 years old) with AI-detected lung nodules at their first visit from March 2021 to February 2022, except for those in the pulmonology or thoracic surgery departments, were retrospectively included. Three radiologists categorized nodules into malignancy, active inflammation, post-inflammatory sequelae, or "other" groups. Characteristics of the nodule and abnormality scores of co-existing lung lesions were compared. Approximately 1% of patients (152/14,563) had unexpected lung nodules. Among 73 patients with follow-up exams, 69.9% had true positive nodules. Increased abnormality scores for nodules were significantly associated with malignancy (odds ratio [OR] 1.076, P = 0.001). Increased abnormality scores for consolidation (OR 1.033, P = 0.040) and pleural effusion (OR 1.025, P = 0.041) were significantly correlated with active inflammation-type nodules. Abnormality scores for fibrosis (OR 1.036, P = 0.013) and nodules (OR 0.940, P = 0.001) were significantly associated with post-inflammatory sequelae categorization. AI-based lesion-detection software of CXRs in daily practice can help identify clinically significant incidental lung nodules, and referring accompanying lung lesions may help classify the nodule.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.

BACKGROUND: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. METHODS: We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. RESULTS: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). CONCLUSIONS: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Targeted Protein Degradation: Principles and Applications of the Proteasome.

The proteasome is a multi-catalytic protease complex that is involved in protein quality control via three proteolytic activities (i.e., caspase-, trypsin-, and chymotrypsin-like activities). Most cellular proteins are selectively degraded by the proteasome via ubiquitination. Moreover, the ubiquitin-proteasome system is a critical process for maintaining protein homeostasis. Here, we briefly summarize the structure of the proteasome, its regulatory mechanisms, proteins that regulate proteasome activity, and alterations to proteasome activity found in diverse diseases, chemoresistant cells, and cancer stem cells. Finally, we describe potential therapeutic modalities that use the ubiquitin-proteasome system.

Expression of Amine Oxidase Proteins in Adrenal Cortical Neoplasm and Pheochromocytoma.

We delved into the expression of amine oxidase family proteins and their potential significance in adrenal gland neoplasm. Tissue microarrays were prepared for 132 cases of adrenal cortical neoplasm (ACN) consisting of 115 cases of adrenal cortical adenoma (ACA), 17 cases of adrenal cortical carcinoma (ACC), and 163 cases of pheochromocytoma (PCC). Immunohistochemical stainings for MAOA, MAOB, LOX, and AOC3 were performed to evaluate the H-scores and compare them with clinicopathological parameters. The H-scores of MAOA (T; p = 0.005) and MAOB (T; p = 0.006) in tumor cells (T) were high in ACN, whereas LOX (T, S; p < 0.001) in tumor and stromal cells (S) and AOC3 (T; p < 0.001) were higher in PCC. In stromal cells, MAOA (S; p < 0.001) and AOC3 (S; p = 0.010) were more expressed in ACA than in ACC. MAOB (S) in PCC showed higher H-scores when the grading of adrenal pheochromocytoma and paraganglioma (GAPP) score was 3 or higher (p = 0.027). In the univariate analysis, low MAOA expression in stromal cells of ACN was associated with shorter overall survival (p = 0.008). In conclusion, monoamine oxidase proteins revealed differences in expression between ACN and PCC and also between benign and malignant cells.

Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow.

Purpose: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. Methods: From January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening facility. Historic reads of three radiologists were used as radiologist interpretation. A commercially-available AI-CAD was used for analysis. One radiologist not involved in interpretation had retrospectively reviewed the abnormality features and assessed the significance (negligible vs. need recall) of the AI-CAD marks. Ground truth in terms of cancer, benign or absence of abnormality was confirmed according to histopathologic diagnosis or negative results on the next-round screen. Results: Of the 6499 mammograms, 6282 (96.7%) were in the negative, 189 (2.9%) were in the benign, and 28 (0.4%) were in the cancer group. AI-CAD detected 5 (17.9%, 5 of 28) of the 9 cancers that were intially interpreted as negative. Of the 648 AI-CAD recalls, 89.0% (577 of 648) were marks seen on examinations in the negative group, and 267 (41.2%) of the AI-CAD marks were considered to be negligible. Stand-alone AI-CAD has significantly higher recall rates (10.0% vs. 3.4%, P < 0.001) with comparable sensitivity and cancer detection rates (P = 0.086 and 0.102, respectively) when compared to the radiologists' interpretation. Conclusion: AI-CAD detected 17.9% additional cancers on screening mammography that were initially overlooked by the radiologists. In spite of the additional cancer detection, AI-CAD had significantly higher recall rates in the clinical workflow, in which 89.0% of AI-CAD marks are on negative mammograms.

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography reimaging after core needle biopsy. Of 276 women who underwent short-term (<3 mo) serial digital mammograms followed by breast cancer surgery from Jan. to Dec. 2017, 550 breasts were included. All core needle biopsies for breast lesions were performed between serial exams. All mammography images were analyzed using a commercially available AI-based software providing an abnormality score (0-100). Demographic data for age, interval between serial exams, biopsy, and final diagnosis were compiled. Mammograms were reviewed for mammographic density and finding. Statistical analysis was performed to evaluate the distribution of variables according to biopsy and to test the interaction effects of variables with the difference in AI-based score according to biopsy. AI-based score of 550 exams (benign or normal in 263 and malignant in 287) showed significant difference between malignant and benign/normal exams (0.48 vs. 91.97 in first exam and 0.62 vs. 87.13 in second exam, P<0.0001). In comparison of serial exams, no significant difference was found in AI-based score. AI-based score difference between serial exams was significantly different according to biopsy performed or not (-0.25 vs. 0.07, P = 0.035). In linear regression analysis, there was no significant interaction effect of all clinical and mammographic characteristics with mammographic examinations performed after biopsy or not. The results from AI-based diagnostic support software for digital mammography was relatively consistent in short-term reimaging even after core needle biopsy.

Frontal peripheral osteomas: a retrospective study.

BACKGROUND: Osteomas are benign, slow-growing bone tumors that can be classified as central, peripheral, or extraskeletal. Central osteomas arise from the endosteum, peripheral osteomas from the periosteum, and extraskeletal osteomas within the muscle. Frontal peripheral osteomas are mainly encountered in plastic surgery. In this study, we retrospectively analyzed the clinical data of patients with frontal peripheral osteomas. METHODS: We retrospectively reviewed the medical records of patients who visited our hospital with frontal peripheral osteomas between January 2014 and June 2022. We analyzed the following variables: age, sex, tumor type (sessile or pedunculated), single or multiple, size, history of head trauma, operation, and recurrence. RESULTS: A total of 39 patients and 41 osteomas were analyzed, of which 29 osteomas (71%) were sessile and 12 osteomas (29%) were pedunculated. The size of the osteomas ranged from 4 to 30 mm, with an average size of 10 mm. The age of patients ranged from 4 to 78 years with a mean age of 52 years. There were seven men (18%) and 32 women (82%), and the man-to-woman ratio was 1:4.6. Two patients (5%) had multiple masses, with two osteomas in each, while only two patients (5%) had a history of head trauma. Twenty-nine patients (74%) underwent ostectomy by a direct approach, and none of the patients experienced recurrence. CONCLUSION: The epidemiologic data of our study will help plastic surgeons encounter frontal peripheral osteomas in the field to provide proper management for their patients.

Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs.

PURPOSE: Detection of early lung cancer using chest radiograph remains challenging. We aimed to highlight the benefit of using artificial intelligence (AI) in chest radiograph with regard to its role in the unexpected detection of resectable early lung cancer. MATERIALS AND METHODS: Patients with pathologically proven resectable lung cancer from March 2020 to February 2022 were retrospectively analyzed. Among them, we included patients with incidentally detected resectable lung cancer. Because commercially available AI-based lesion detection software was integrated for all chest radiographs in our hospital, we reviewed the clinical process of detecting lung cancer using AI in chest radiographs. RESULTS: Among the 75 patients with pathologically proven resectable lung cancer, 13 (17.3%) had incidentally discovered lung cancer with a median size of 2.6 cm. Eight patients underwent chest radiograph for the evaluation of extrapulmonary diseases, while five underwent radiograph in preparation of an operation or procedure concerning other body parts. All lesions were detected as nodules by the AI-based software, and the median abnormality score for the nodules was 78%. Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report. Total and invasive sizes of the part-solid nodules were 2.3-3.3 cm and 0.75-2.2 cm, respectively. CONCLUSION: This study demonstrates actual cases of unexpectedly detected resectable early lung cancer using AI-based lesion detection software. Our results suggest that AI is beneficial for incidental detection of early lung cancer in chest radiographs.

Primary hepatic mucosa-associated lymphoid tissue lymphoma mimicking hepatocellular carcinoma in a patient with chronic hepatitis B: a case report.

Primary hepatic lymphoma is a rare disease, and primary hepatic mucosa-associated lymphoid tissue (MALT) lymphoma accounts for only 0.3% of all primary hepatic lymphomas. Herein, we report a case of primary hepatic MALT lymphoma in a male patient in his mid-40 s with chronic hepatitis B infection. The patient visited our department for further examination of a hepatic nodule initially visualized through abdominal pelvic computed tomography (CT). Based on imaging studies and elevated levels of tumor markers, the tumor was suspected to be hepatocellular carcinoma. A laparoscopic inferior sectionectomy (segment 5 and 6) was performed, and immunohistochemical staining revealed that the tumor was positive for CD20, B-cell lymphoma 2, pan-cytokeratin (CK), and CK19 markers. Pathological findings revealed it to be a primary hepatic MALT lymphoma. After surgery, bone marrow biopsies and fluorodeoxyglucose-positron emission tomography integrated with CT scanning confirmed that there was no other involvement. The patient did not receive chemotherapy, and there was no recurrence during the 24-month follow-up period. Hepatocellular carcinoma is the most common malignancy in patients with chronic hepatitis B, but rare tumors such as primary MALT lymphoma can also occur, so a careful approach is required for their differentiation.

Relationship between self-consciousness and empathy for cancer patients among nursing students: The moderating effects of clinical practice satisfaction.

BACKGROUND: Patients with cancer experience physical and mental difficulties that can be relieved with the support and empathy of healthcare professionals. Empathy can be affected by self-consciousness. The clinical performance and interpersonal competence of nursing students are related to their satisfaction with clinical practice. OBJECTIVE: This study explored the moderating effect of clinical practice satisfaction of nursing students on the relationship between self-consciousness and empathy for patients with cancer. DESIGN: Cross-sectional descriptive study. SETTING: Three colleges of nursing in South Korea. PARTICIPANTS: A total of 136 senior nursing students across three universities. METHODS: The participants completed an online questionnaire on demographic and education-related characteristics, self-consciousness, and empathy competency. We used the Korean versions of the Self-Consciousness Scale and Empathy Construct Rating Scale. The overall response rate was 42.5 %. SPSS PROCESS macro was used to test the moderating role of satisfaction with clinical practice. RESULTS: Private self-consciousness was significantly associated with clinical practice satisfaction and empathy. The relationship between practice satisfaction and empathy was significantly positive. In addition, the satisfaction of the nursing students with clinical practice moderated the association between private self-consciousness and empathy for patients with cancer. Empathy was more affected by private self-consciousness among senior nursing students who were less satisfied with clinical practice than among those who were more satisfied with clinical practice. CONCLUSION: To improve empathy for patients with cancer, educational strategies must be created to improve the private self-consciousness and satisfaction with the clinical practice of nursing students.

Comparison of the clinical usefulness of structured and free-text reports for interpretation of jaw lesions on cone beam computed tomography images.

OBJECTIVE: This study compared the clinical usefulness of structured reports (SRs) and free-text reports (FTRs) of lesions depicted on cone beam computed tomography (CBCT) images from the perspectives of report providers and receivers. STUDY DESIGN: In total, 36 CBCT images of jaw lesions obtained between February 2020 and August 2020 were evaluated. A working group of 3 oral and maxillofacial radiologists (OMRs) established a reporting system and prepared reports. Evaluation group I (2 OMRs) wrote SRs and FTRs for each case and assessed the reporting process for the criteria of convenience and organization. Evaluation group II (3 general practitioners [GPs] and 3 oral and maxillofacial surgeons [OMSs]) assessed the reports for the criteria of productivity, consistency, and organization. A 5-point Likert scale was used to assess the usefulness of each report. Scores were statistically compared according to report type with the paired Wilcoxon signed-rank test. RESULTS: The SRs scored significantly higher for all criteria as assessed by evaluation group I and the GPs of group II (P < .001). The FTRs scored significantly higher for productivity and organization as assessed by the OMSs of group II (P = .005 for both criteria). CONCLUSIONS: The clinical usefulness of reports may differ according to roles of the report recipients in diagnosis and treatment.

Inhibitory effects of Centella asiatica (L.) Urban on enlarged prostate through androgen receptor and PI3K/Akt signaling pathways.

Centella asiatica (L.) Urban (C. asiatica) is a traditional herbal medicine that has been used for wound healing and anti-inflammation since ancient times. Various biological effects of C. asiatica ethanolic extract (CAE) were previously reported. However, in our previous study, C. asiatica aqueous extract (CAA) exhibited higher inhibitory activity on benign prostatic hyperplasia (BPH) than CAE. Therefore, the aim of this study was to investigate the effect of CAA on BPH, and elucidate the inhibitory mechanism through in vitro and in vivo experiments as well as metabolite analysis of CAA. A BPH rat model was induced by daily subcutaneous injection of testosterone propionate (TP, 3 mg kg-1) dissolved in corn oil for 4 weeks after castration. The experimental group, the CAA treatment group, was orally administered CAA (100 mg kg-1) for 4 weeks while inducing prostatic hyperplasia. Saw palmetto extract (Saw, 100 mg kg-1) and Finasteride (Fi, 1 mg kg-1) were used as positive controls and were administered orally for 4 weeks. CAA significantly inhibited androgen receptor signaling related factors overexpressed by dihydrotestosterone (DHT) treatment in prostate cell lines. Afterwards, the testosterone-induced BPH model was used to verify the alleviation efficacy of CAA in prostatic hyperplasia. Prostate size and the thickness of the prostate tissue epithelium were significantly decreased in the group treated with CAA compared to those in the BPH group. The results of protein expression in the prostate tissue confirmed that CAA inhibited androgen receptor signaling in BPH and decreased the expression of growth factors. Moreover, CAA suppressed the expression of the PI3K/Akt pathway and cell proliferation-related factors compared to the BPH group. Taken together, these results indicate that CAA improves the inhibitory efficacy of BPH by inhibiting the androgen receptor and PI3K/Akt pathways, suggesting that CAA may be a promising candidate for biopharmaceutical formulations of BPH.